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COMPUTATIONAL NEUROSCIENCE

Co-Coordinators

Efstratios K. Kosmidis, Assistant Professor of Neurophysiology

Vassilis Cutsuridis, Affiliated Researcher, FORTH, Senior Lecturer, School of Computer Science, University of Lincoln, UK

Teaching hours and weekly schedule

This a 2nd semester 2 weeks elective course that corresponds to 3 ECTs and 36 total hours of teaching; 24 hours lectures and 12 hours of student presentation.

The weekly schedule could be according to the table below. The teaching hours/day may be increased if necessary

Weekly Program for 3 ECTs

3 ECTs=21 hours of teaching+15 hours student presentations

Total: 2 weeks Χ teaching hours/day

Monday

4

8

Tuesday

4

8

Wednesday

4

8

Thursday

4

8

Friday

4

8

 

 

40

Description

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing neural function from the single neuron to the neural network level. Specific topics will cover synaptic plasticity, learning and memory in the brain. We will make use of C++/Matlab/NEURON demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is aimed to students of all ages eager to learn how the brain processes information.

Course Overview

Computational neuroscience is the intersection of neurophysiology, neuroanatomy, mathematical modeling and computer science. Its primary target is to describe how the brain “computes” by simplifying neuronal biology to a set of equations. As most branches of Science, it contains elements of Philosophy and Art. Emphasis will be given on mathematical descriptions and computational techniques used to study and understand neurons and network of neurons. Weekly assignments will allow students to learn through direct experience. The course will provide a glimpse of this exciting field aiming to motivate the young mind by covering the following topics:

•             Mathematical modeling in neurophysiology. An introduction.

•             Classical, membrane potential theory

•             Electrical analogue of the cell membrane – The Lapique model (Leaky Integrate and Fire)

•             Action potential theory. The Hodgkin – Huxley model

•             Cable theory, multi-compartmental single neuron model

•             Models of synaptic transmission (AMPA, NMDA, GABAA, GABAB)

•             Models of synaptic plasticity (LTP/LTD, STDP, Hebbian, Delta rule, backpropagation, etc)

•             Models of neural networks (feedforward, feedback, competitive, etc)

•             Computational tools (NEURON and MATLAB)

Skills & Learning Outcomes

Upon successful completion of this course, students will be able to:

1.            Understand and appreciate the integral role of computational techniques and concepts in neuroscience. Study and critique review papers relating the use of computational techniques to the broader development of theories and experimental methods in neuroscience.

2.            Understand basic concepts for ion channel and single cell modeling, possibly including:

a.            I-V curves, the Hodgkin-Huxley model of action potential generation, and simple kinetic models of ion channels, integrate-and-fire approximation

b.            Mathematical representations of conductances, currents, and their relationship to dynamic changes in nerve cell behavior

3.            Use these models and associated methods to predict qualitative functional outcomes or quantitative state changes when varying parameters or changing structural properties of the models.

4.            Use one or more software tool that facilitates the calculation of such predictions.

 

Titles of the lectures and the names of the lecturers

 

A/A

Lecture Hours

Computational Neuroscience

Lecturer

 

 

Lecture title

 

1

3

Mathematical modeling in neurophysiology. An introduction.

Efstratios Kosmidis

2

3

Classical, membrane potential theory

Efstratios Kosmidis

3

3

Electrical analogue of the cell membrane – The Lapique model (Leaky Integrate and Fire)

Efstratios Kosmidis

4

3

Action potential theory. The Hodgkin – Huxley model

Efstratios Kosmidis

5

3

Dendritic functional properties. Multi-compartmental modeling

Panagiota. Poirazi

6

3

Kinetic models of synaptic transmission

Vassilis Cutsuridis

7

3

Computational tools

Vassilis Cutsuridis

8

3

Synaptic plasticity, learning and memory modeling approaches

Vassilis Cutsuridis

9

3

Network models

Vassilis Cutsuridis

 

Total Lecture hours

24

 

 

Total student presentation hours

12

 

 

Total teaching hours

36